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The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions, and libjpeg, which can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. ...

...This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging Face Inference Toolkit implements various additional environment variables to simplify your deployment experience. The Hugging Face Inference Toolkit allows user to override the default methods of the HuggingFaceHandlerService. ...

...A facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive". Bias detection and mitigation for datasets and models. The label is a column or feature which is the target for training a machine learning model. The label can have value(s) that designates that sample as having a "positive" outcome. A bias measure is a function that returns a bias metric. A bias metric is a numerical value indicating the level of bias detected as determined by a particular bias measure. A collection of bias metrics for a given dataset or a combination of a dataset and model.

SageMaker Chainer Containers is an open-source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images. Amazon SageMaker utilizes Docker containers to run all training jobs & inference endpoints. The Docker images are built from the Dockerfiles specified in Docker/. The Docker files are grouped based on Chainer version and separated based on Python version and processor type. ...

...The diagram is fully interactive: entity can be added/removed from the diagram and shuffled; events can be filtered, searched, highlighted and annotated with comments. MSCViewer features integration with a Python interpreter which allows writing Python scripts interacting with the model. This powerful feature can be used to automate validatation of distributed control flows, integrate with graphing infrastructure, etc.

...Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide controllable adjustment range, which thereby makes it possible to provide optional modes of recommendation aiming different kinds of users.

This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.

DEVSimPy is an advanced wxPython GUI for the modeling and simulation of systems based on the DEVS (Discrete EVent system Specification) formalism. Features include powerful built-in editor, advanced modeling approach, powerful discrete event simulation algorithm, import/export DEVS components library and more.